Integer‐Valued Autoregressive Models With Survival Probability Driven By A Stochastic Recurrence Equation
提出一类生存概率由随机递归方程驱动的整数值自回归模型，能捕捉时变依赖关系，提升模型拟合与预测效果，并通过犯罪数据实证验证，同时证明了误设定下极大似然估计的一致性。
This paper proposes a new class of integer‐valued autoregressive models with a dynamic survival probability. The peculiarity of this class of models lies in the specification of the survival probability through a stochastic recurrence equation. The proposed models can effectively capture changing dependence over time and enhance both the in‐sample and out‐of‐sample performance of integer‐valued autoregressive models. This point is illustrated through an empirical application to a real‐time series of crime reports. Additionally, this paper discusses the reliability of likelihood‐based inference for the class of models. In particular, this study proves the consistency of the maximum likelihood estimator and a plug‐in estimator for the conditional probability mass function in a misspecified model setting.